This article is cited in 1 scientific paper (total in 1 paper)
A hybrid heuristic parallel method of global optimization
K. V. Pushkaryov, V. D. Koshur
Siberian Federal University, Krasnoyarsk
The problem of finding the global minimum of a continuous objective function of multiple variables in a multidimensional parallelepiped is considered. A hybrid heuristic parallel method for solving of complicated global optimization problems is proposed. The method is based on combining various methods and on the multi-agent technology. It consists of new methods (for example, the method of neural network approximation of inverse coordinate mappings that uses Generalized Regression Neural Networks (GRNN) to map the values of an objective function to coordinates) and modified classical methods (for example, the modified Hooke-Jeeves method). An implementation of the proposed method as a cross-platform (on the source code level) library written in the C++ language is briefly discussed. This implementation uses the message passing via MPI (Message Passing Interface). The method is compared with 21 modern methods of global optimization and with a genetic algorithm using 28 test objective functions of 50 variables.
C++, MPI, global optimization, heuristic methods, neural networks, parallel computing, C++, MPI.
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K. V. Pushkaryov, V. D. Koshur, “A hybrid heuristic parallel method of global optimization”, Num. Meth. Prog., 16:2 (2015), 242–255
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\by K.~V.~Pushkaryov, V.~D.~Koshur
\paper A hybrid heuristic parallel method of global optimization
\jour Num. Meth. Prog.
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K. V. Pushkarev, “Globalnaya optimizatsiya na osnove neirosetevoi approksimatsii inversnykh zavisimostei s evolyutsionnym upravleniem parametrami”, Programmnye sistemy: teoriya i prilozheniya, 10:2 (2019), 33–65
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